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evaluate.py
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evaluate.py
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import os
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
import numpy as np, argparse, time, pickle, random
import torch
import torch.nn as nn
import torch.optim as optim
from dataloader import IEMOCAPDataset
from model import *
from sklearn.metrics import f1_score, confusion_matrix, accuracy_score, classification_report, \
precision_recall_fscore_support
from trainer import train_or_eval_model, save_badcase
from dataset import IEMOCAPDataset
from dataloader import get_IEMOCAP_loaders
from transformers import AdamW
import copy
# We use seed = 100 for reproduction of the results reported in the paper.
seed = 100
def seed_everything(seed=seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
def evaluate(model, dataloader, cuda, args, speaker_vocab, label_vocab):
preds, labels = [], []
scores, vids = [], []
dialogs = []
speakers = []
model.eval()
for data in dataloader:
features, label, adj,s_mask, s_mask_onehot,lengths, speaker, utterances = data